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Using Nudges to Prevent Student Dropouts in the Pandemic [PDF]

open access: yesSSRN Electronic Journal, 2020
The impacts of COVID-19 reach far beyond the hundreds of lives lost to the disease; in particular, the pre-existing learning crisis is expected to be magnified during school shutdown. Despite efforts to put distance learning strategies in place, the threat of student dropouts, especially among adolescents, looms as a major concern. Are interventions to
Lichand G, Christen J.
europepmc   +4 more sources

All-Year Dropout Prediction Modeling and Analysis for University Students

open access: yesApplied Sciences, 2023
The core of dropout prediction lies in the selection of predictive models and feature tables. Machine learning models have been shown to predict student dropouts accurately.
Zihan Song   +3 more
doaj   +1 more source

Student Dropout Prediction [PDF]

open access: yes, 2020
Among the many open problems in the learning process, students dropout is one of the most complicated and negative ones, both for the student and the institutions, and being able to predict it could help to alleviate its social and economic costs. To address this problem we developed a tool that, by exploiting machine learning techniques, allows to ...
Del Bonifro F.   +3 more
openaire   +3 more sources

University Dropout in Engineering: Motives and Student Trajectories [PDF]

open access: yesPsicothema, 2021
Dropout in higher education is a concern for students, families, educational institutions, and society. Tertiary education is an important mechanism for empowering people and STEM courses are vital to countries' development.The study combined quantitative and qualitative data.
Casanova, Joana R.   +3 more
openaire   +3 more sources

Why do open and distance education students drop out? Views from various stakeholders

open access: yesInternational Journal of Educational Technology in Higher Education, 2022
While the demand for open and distance education is increasing, it also faces high dropout rates. The reasons and solutions for student dropouts need considerable attention.
Ayşe Bağrıacık Yılmaz   +1 more
doaj   +1 more source

Dropout, student lag and successful completion in 40 cohorts of the Medicine Program of the Universidad Tecnológica de Pereira. Colombia

open access: yesIatreia, 2022
Introduction: Dropout rates are a constant concern for schools of medicine. Its study and solution are complex, it compromises the future of the student and affects the academic reputation of the program.
Cabrales, Rodolfo Adrián   +4 more
doaj   +1 more source

Deep FM-Based Predictive Model for Student Dropout in Online Classes

open access: yesIEEE Access, 2023
The student’s high dropout rate is a severe issue in online learning courses. As a result, it is creating concerns for academics and administrators in the field of education. A practical method of preventing dropouts is predicting students’
Nuha Mohammed Alruwais
doaj   +1 more source

HIGH SCHOOL DROPOUT DILEMMA IN AMERICA AND THE IMPORTANCE OF REFORMATION OF EDUCATION SYSTEMS TO EMPOWER ALL STUDENTS

open access: yesInternational Journal of Modern Education Studies, 2022
Out of school happens when a student withdraws themselves from school at any level of education without a certificate to account for their education. It is an educational problem in America because of its negative consequences on society.
Theodoto Ressa, Allyson Andrews
doaj   +1 more source

Predicting Student Dropout and Academic Success

open access: yesData, 2022
Higher education institutions record a significant amount of data about their students, representing a considerable potential to generate information, knowledge, and monitoring. Both school dropout and educational failure in higher education are an obstacle to economic growth, employment, competitiveness, and productivity, directly impacting the lives ...
Valentim Realinho   +3 more
openaire   +2 more sources

Student Dropout Prediction

open access: yesInternational Journal on Emerging Trends in Technology
Student dropout poses a major challenge to educational institutions, affecting academic performance and institutional reputation. This study applies machine learning techniques to predict at-risk students using data from the Department of Computer Science, University of Benin (2016–2020), with 906 records analyzed. Six classifiers—Naive Bayes, Logistic
Dr. Priti Sanjekar   +4 more
  +4 more sources

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